#### 144_Renuka_Asane

Extra_Link - https://colab.research.google.com/drive/1uiZIGT3aNRXx0Eto81l_-VxYUOFXQlEe

Assignment No - 1 Simple Linear Regression

#STEP-1: Import Libraries
# Code to read csv file into colaboratory:
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

WARNING:root:pydrive is deprecated and no longer maintained. We recommend that you migrate your projects to pydrive2, the maintained fork of pydrive

#STEP-2: Autheticate E-Mail ID
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

#installation of python libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Load dataset
data = pd.read_csv('SLR-Data.csv')
print(data.shape)
print(data.head())
#print(data.describe())

# Collecting X and Y
X = data['No of Hours Spent During(X)'].values
Y = data['Risk Score on a scale of 0-100(Y)'].values

# Calculate Mean X and Y
mean_x = np.mean(X)
mean_y = np.mean(Y)
#print(mean_x)
#print(mean_y)
# Total number of values
m = len(X)
# Using the formula to calculate b1(slope) and b0(intercept)
numer = 0
denom = 0
for i in range(m):
    numer += (X[i] - mean_x) * (Y[i] - mean_y)
    denom += (X[i] - mean_x) ** 2
b1 = numer / denom
b0 = mean_y - (b1 * mean_x)

# Print coefficients:b1,b0
print("Slope,Intercept:",b1,b0)

# Plotting Values and Regression Line
max_x = np.max(X)
min_x = np.min(X)

# Calculating line values x and y
x = np.linspace(min_x, max_x)
y = b0 + b1 * x

# Ploting Line
plt.plot(x, y, color='green', label='Regression Line')
# Ploting Scatter Points
plt.scatter(X, Y, c='blue', label='Scatter Plot')
plt.xlabel('No of Hours Spent During')
plt.ylabel('Risk Score on a scale of 0-100')
plt.legend()
plt.show()

#For  Calculating Root Mean Squares Error
rmse = 0
for i in range(m):
    y_pred = b0 + b1 * X[i]
    rmse += (Y[i] - y_pred) ** 2
rmse = np.sqrt(rmse/m)
print("Root Mean Squares Error:",rmse)
# Calculating Accuracy Score
ss_t = 0
ss_r = 0
for i in range(m):
    y_pred = b0 + b1 * X[i]
    ss_t += (Y[i] - mean_y) ** 2
    ss_r += (Y[i] - y_pred) ** 2
r2 = 1 - (ss_r/ss_t)
print("Accuracy:",r2*100)
#predicting a o/p (y) for new value of x
predict_x=int(input('Enter No Hours Spent in Driving:'))
predict_y=(4.58789861*predict_x)+12.584627964022907
plt.scatter(X,Y)
plt.scatter(predict_x,predict_y)
plt.xlabel('No Hours Spent Driving(Predicted_x)')
plt.ylabel('Risk Score on a Scale of 0-100(Predicted_y)')
 #plotting the regression line
plt.scatter(X, Y, c='blue')
plt.plot(x, y, color='green')
# function to show plot
plt.show()

(8, 2)
   No of Hours Spent During(X)  Risk Score on a scale of 0-100(Y)
0                           10                                 95
1                            9                                 80
2                            2                                 10
3                           15                                 50
4                           10                                 45
Slope,Intercept: 4.58789860997547 12.584627964022893

Root Mean Squares Error: 22.759716640449565
Accuracy: 43.709481451010035
Enter No Hours Spent in Driving:3

Assignment No - 2 Principle Component Analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
%matplotlib inline

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
df = pd.read_csv(url
                 , names=['sepal length','sepal width','petal length','petal width','target'])
df.head()

	sepal length	sepal width	petal length	petal width	target
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

features = ['sepal length', 'sepal width', 'petal length', 'petal width']
x = df.loc[:, features].values

y = df.loc[:,['target']].values

x = StandardScaler().fit_transform(x)

pd.DataFrame(data = x, columns = features).head()

	sepal length	sepal width	petal length	petal width
0	-0.900681	1.032057	-1.341272	-1.312977
1	-1.143017	-0.124958	-1.341272	-1.312977
2	-1.385353	0.337848	-1.398138	-1.312977
3	-1.506521	0.106445	-1.284407	-1.312977
4	-1.021849	1.263460	-1.341272	-1.312977

#PCA Projection to 2D
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
             , columns = ['principal component 1', 'principal component 2'])
principalDf.head(5)

	principal component 1	principal component 2
0	-2.264542	0.505704
1	-2.086426	-0.655405
2	-2.367950	-0.318477
3	-2.304197	-0.575368
4	-2.388777	0.674767

df[['target']].head()

	target
0	Iris-setosa
1	Iris-setosa
2	Iris-setosa
3	Iris-setosa
4	Iris-setosa

finalDf = pd.concat([principalDf, df[['target']]], axis = 1)
finalDf.head(5)

	principal component 1	principal component 2	target
0	-2.264542	0.505704	Iris-setosa
1	-2.086426	-0.655405	Iris-setosa
2	-2.367950	-0.318477	Iris-setosa
3	-2.304197	-0.575368	Iris-setosa
4	-2.388777	0.674767	Iris-setosa

fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title('2 Component PCA', fontsize = 20)


targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
colors = ['r', 'g', 'b']
for target, color in zip(targets,colors):
    indicesToKeep = finalDf['target'] == target
    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']
               , finalDf.loc[indicesToKeep, 'principal component 2']
               , c = color
               , s = 50)
ax.legend(targets)
ax.grid()

#Explained Variance:The explained variance tells us how much information (variance)
#can be attributed to each of the principal components.
pca.explained_variance_ratio_

array([0.72770452, 0.23030523])

Assignment 3 Decision Tree

downloaded = drive.CreateFile({'id':'1jql2mwV15BCFeX52G1PGSCr8Y4jLdn8f'}) # replace the id with id of file you want to access
downloaded.GetContentFile('DT-Data.csv')

#import packages
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#reading Dataset
dataset=pd.read_csv("DT-Data.csv")
X=dataset.iloc[:,:-1]
y=dataset.iloc[:,5].values

#Perform Label encoding
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()

X = X.apply(LabelEncoder().fit_transform)
print (X)

from sklearn.tree import DecisionTreeClassifier
regressor=DecisionTreeClassifier()
regressor.fit(X.iloc[:,1:5],y)

#Predict value for the given expression
X_in=np.array([0,1,0,1])

y_pred=regressor.predict([X_in])
print ("Prediction:", y_pred)

from six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
# Create DOT data
dot_data = StringIO()

export_graphviz(regressor, out_file=dot_data, filled=True, rounded=True, special_characters=True)
# Draw graph
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('Decision_Tree.png')
# Show graph
Image(graph.create_png())

    id  age  income  gender  marital_status
0    0    1       0       1               1
1    1    1       0       1               0
2    2    0       0       1               1
3    3    2       2       1               1
4    4    2       1       0               1
5    5    2       1       0               0
6    6    0       1       0               0
7    7    1       2       1               1
8    8    1       1       0               0
9    9    2       2       0               1
10  10    1       2       0               0
11  11    0       2       1               0
12  12    0       0       0               1
13  13    2       2       1               0
Prediction: ['Yes']

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
  warnings.warn(

OR

# Load dataset
data = pd.read_csv('data.csv')

#import packages
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#reading Dataset
dataset=pd.read_csv("data.csv")
X=dataset.iloc[:,:-1]
y=dataset.iloc[:,5].values

#Perform Label encoding
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()

X = X.apply(LabelEncoder().fit_transform)
print (X)

from sklearn.tree import DecisionTreeClassifier
regressor=DecisionTreeClassifier()
regressor.fit(X.iloc[:,1:5],y)

#Predict value for the given expression
X_in=np.array([0,1,0,1])

y_pred=regressor.predict([X_in])
print ("Prediction:", y_pred)

from six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
# Create DOT data
dot_data = StringIO()

export_graphviz(regressor, out_file=dot_data, filled=True, rounded=True, special_characters=True)
# Draw graph
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
#graph.write_png('Decision_Tree.png')
# Show graph
Image(graph.create_png())

     footfall  tempMode  AQ  USS  CS  VOC  RP  IP  Temperature
0           0         7   6    0   5    6  17   2            0
1          71         1   2    2   4    1   1   3            0
2          29         7   1    1   5    1   5   5            0
3          58         4   2    3   4    1   9   5            0
4          90         7   4    5   3    0  49   5            0
..        ...       ...  ..  ...  ..  ...  ..  ..          ...
939         0         7   6    0   5    4  54   5           23
940         0         7   4    1   5    6  31   5           23
941         0         3   5    1   6    5  24   5           23
942         0         6   5    1   4    6  27   6           23
943        18         7   3    1   5    3  42   6           23

[944 rows x 9 columns]
Prediction: [5]

/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names
  warnings.warn(

dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.784556 to fit

Assignment no 4 Naive Bayes

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix

#from sklearn.datasets import load_iris
#dataset = load_iris()
#dataset
downloaded = drive.CreateFile({'id':'12BY34aCbYLoLjy3gDUMrZEBUf7l5FZsd'}) # replace the id with id of file you want to access
downloaded.GetContentFile('iris.csv')
dataset=pd.read_csv("iris.csv")
dataset
#https://drive.google.com/file/d/12BY34aCbYLoLjy3gDUMrZEBUf7l5FZsd/view?usp=share_link

	sepal.length	sepal.width	petal.length	petal.width	variety
0	5.1	3.5	1.4	0.2	Setosa
1	4.9	3.0	1.4	0.2	Setosa
2	4.7	3.2	1.3	0.2	Setosa
3	4.6	3.1	1.5	0.2	Setosa
4	5.0	3.6	1.4	0.2	Setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	Virginica
146	6.3	2.5	5.0	1.9	Virginica
147	6.5	3.0	5.2	2.0	Virginica
148	6.2	3.4	5.4	2.3	Virginica
149	5.9	3.0	5.1	1.8	Virginica

150 rows × 5 columns

#Spliting the dataset in independent and dependent variables
X = dataset.iloc[:,:4].values
y = dataset['variety'].values

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 82)

# Fitting Naive Bayes Classification to the Training set with linear kernel
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)

# Fitting Naive Bayes Classification to the Training set with linear kernel
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)

# Fitting Naive Bayes Classification to the Training set with linear kernel
nb = GaussianNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)

# Calculate the confusion matrix and assign it to 'cm'
cm = confusion_matrix(y_test, y_pred)

#finding accuracy from the confusion matrix.
a = cm.shape
corrPred = 0
falsePred = 0

for row in range(a[0]):
    for c in range(a[1]):
        if row == c:
            corrPred +=cm[row,c]
        else:
            falsePred += cm[row,c]
print('Correct predictions: ', corrPred)
print('False predictions', falsePred)
print ('\n\nAccuracy of the Naive Bayes Clasification is: ', corrPred/(cm.sum()))

Correct predictions:  28
False predictions 2


Accuracy of the Naive Bayes Clasification is:  0.9333333333333333

Assignment no - 5 SVM

# Load the important packages
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.svm import SVC

# Load the datasets
cancer = load_breast_cancer()
X = cancer.data[:, :2]
y = cancer.target

#Build the model
svm = SVC(kernel="linear")
# Trained the model
svm.fit(X, y)

# Plot Decision Boundary
DecisionBoundaryDisplay.from_estimator(
		svm,
		X,
		response_method="predict",
		cmap=plt.cm.Spectral,
		alpha=0.8,
		xlabel=cancer.feature_names[0],
		ylabel=cancer.feature_names[1],
	)

# Scatter plot
plt.scatter(X[:, 0], X[:, 1],
			c=y,
			s=20, edgecolors="k")
plt.show()

#Build the model
svm = SVC(kernel="rbf", gamma=0.5, C=1.0)
# Trained the model
svm.fit(X, y)

# Plot Decision Boundary
DecisionBoundaryDisplay.from_estimator(
		svm,
		X,
		response_method="predict",
		cmap=plt.cm.Spectral,
		alpha=0.8,
		xlabel=cancer.feature_names[0],
		ylabel=cancer.feature_names[1],
	)

# Scatter plot
plt.scatter(X[:, 0], X[:, 1],
			c=y,
			s=20, edgecolors="k")
plt.show()

Assignment 6 - K-Means

#import packages
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#create dataset using DataFrame
df=pd.DataFrame({'X':[0.1,0.15,0.08,0.16,0.2,0.25,0.24,0.3],
                 'y':[0.6,0.71,0.9,0.85,0.3,0.5,0.1,0.2]})
f1 = df['X'].values
f2 = df['y'].values
X = np.array(list(zip(f1, f2)))
print(X)

#centroid points
C_x=np.array([0.1,0.3])
C_y=np.array([0.6,0.2])
centroids=C_x,C_y

#plot the given points
colmap = {1: 'r', 2: 'b'}
plt.scatter(f1, f2, color='k')
plt.show()

#for i in centroids():
plt.scatter(C_x[0],C_y[0], color=colmap[1])
plt.scatter(C_x[1],C_y[1], color=colmap[2])
plt.show()

C = np.array(list((C_x, C_y)), dtype=np.float32)
print (C)

#plot given elements with centroid elements
plt.scatter(f1, f2, c='#050505')
print("point No.6[0.25,0.5] is belongs to blue cluster(cluster no:2)")
plt.scatter(C_x[0], C_y[0], marker='*', s=200, c='r')
plt.scatter(C_x[1], C_y[1], marker='*', s=200, c='b')
plt.show()


#import KMeans class and create object of it
from sklearn.cluster import KMeans
model=KMeans(n_clusters=2,random_state=0)
model.fit(X)
labels=model.labels_
print(labels)

#using labels find population around centroid
count=0
for i in range(len(labels)):
    if (labels[i]==1):
        count=count+1

print('No of population around cluster 2:',count-1)

#Find new centroids
new_centroids = model.cluster_centers_

print('Previous value of m1 and m2 is:')
print('M1==',centroids[0])
print('M1==',centroids[1])

print('Updated value of m1 and m2 is:')
print('M1==',new_centroids[0])
print('M1==',new_centroids[1])

[[0.1  0.6 ]
 [0.15 0.71]
 [0.08 0.9 ]
 [0.16 0.85]
 [0.2  0.3 ]
 [0.25 0.5 ]
 [0.24 0.1 ]
 [0.3  0.2 ]]

[[0.1 0.3]
 [0.6 0.2]]
point No.6[0.25,0.5] is belongs to blue cluster(cluster no:2)

[1 1 1 1 0 0 0 0]
No of population around cluster 2: 3
Previous value of m1 and m2 is:
M1== [0.1 0.3]
M1== [0.6 0.2]
Updated value of m1 and m2 is:
M1== [0.2475 0.275 ]
M1== [0.1225 0.765 ]

#STEP-1: Import Libraries
# Code to read csv file into colaboratory:
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

#STEP-2: Autheticate E-Mail ID
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

#STEP-3: Get File from Drive using file-ID

downloaded = drive.CreateFile({'id':'1e10Ynfgrc35FtMl2V5qpzTGyuWF4KQsZ'}) # replace the id with id of file you want to access
downloaded.GetContentFile('hw.csv')

Assignment 7 - Gradient Boost Classifier

# Import all relevant libraries

from sklearn.ensemble import GradientBoostingClassifier

import numpy as np

import pandas as pd

from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, confusion_matrix

from sklearn import preprocessing

import warnings

warnings.filterwarnings("ignore")

#STEP-1: Import Libraries
# Code to read csv file into colaboratory:
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

#STEP-2: Autheticate E-Mail ID
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

# Get File from Drive using file-ID
downloaded = drive.CreateFile({'id':'1zI-X3zdiuM9u74zQyKIShvAUtPJQ7jUK'}) # replace the id with id of file you want to access
downloaded.GetContentFile('income_evaluation.csv')
# https://drive.google.com/file/d/1zI-X3zdiuM9u74zQyKIShvAUtPJQ7jUK/view?usp=sharing  (Dataset Downloads Link)

#Now let’s read the dataset and look at the columns to understand the information better.
#https://drive.google.com/file/d/1zI-X3zdiuM9u74zQyKIShvAUtPJQ7jUK/view?usp=sharing
df = pd.read_csv('income_evaluation.csv')
df.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country	income
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K

df.shape

(32561, 15)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32561 entries, 0 to 32560
Data columns (total 15 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   age              32561 non-null  int64 
 1    workclass       32561 non-null  object
 2    fnlwgt          32561 non-null  int64 
 3    education       32561 non-null  object
 4    education-num   32561 non-null  int64 
 5    marital-status  32561 non-null  object
 6    occupation      32561 non-null  object
 7    relationship    32561 non-null  object
 8    race            32561 non-null  object
 9    sex             32561 non-null  object
 10   capital-gain    32561 non-null  int64 
 11   capital-loss    32561 non-null  int64 
 12   hours-per-week  32561 non-null  int64 
 13   native-country  32561 non-null  object
 14   income          32561 non-null  object
dtypes: int64(6), object(9)
memory usage: 3.7+ MB

df.isnull().sum()

	0
age	0
workclass	0
fnlwgt	0
education	0
education-num	0
marital-status	0
occupation	0
relationship	0
race	0
sex	0
capital-gain	0
capital-loss	0
hours-per-week	0
native-country	0
income	0

dtype: int64

#df.drop(columns=' fnlwgt',inplace=True)
df.columns

Index(['age', ' workclass', ' fnlwgt', ' education', ' education-num',
       ' marital-status', ' occupation', ' relationship', ' race', ' sex',
       ' capital-gain', ' capital-loss', ' hours-per-week', ' native-country',
       ' income'],
      dtype='object')

X = df.drop(columns=' income')
y = df[' income']

from sklearn.preprocessing import LabelEncoder

def label_encoder(a):
    le = LabelEncoder()
    df[a] = le.fit_transform(df[a])

df.columns

Index(['age', ' workclass', ' fnlwgt', ' education', ' education-num',
       ' marital-status', ' occupation', ' relationship', ' race', ' sex',
       ' capital-gain', ' capital-loss', ' hours-per-week', ' native-country',
       ' income'],
      dtype='object')

label_list = [' workclass', ' education',' marital-status',
       ' occupation', ' relationship', ' race', ' sex',' native-country', ' income']

label_list = [' workclass', ' education',' marital-status',
       ' occupation', ' relationship', ' race', ' sex',' native-country', ' income']

df.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country	income
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K

# Assuming 'df' is your DataFrame

# 1. Identify categorical features
categorical_features = df.select_dtypes(include=['object']).columns

# 2. Create a LabelEncoder for each categorical feature
label_encoders = {}
for feature in categorical_features:
    label_encoders[feature] = LabelEncoder()
    df[feature] = label_encoders[feature].fit_transform(df[feature])

# 3. Now proceed with your model training
X = df.drop([' income'],axis=1).values   # independant features
y = df[' income'].values

print("X_train shape:",X_train.shape)
print("y_test shape:",y_test.shape)
print("X_test shape:",X_test.shape)
print("y_train shape:",y_train.shape)

X_train shape: (26048, 14)
y_test shape: (6513,)
X_test shape: (6513, 14)
y_train shape: (26048,)

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder

# Choose your test size to split between training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

gradient_booster = GradientBoostingClassifier(learning_rate=0.1)
accuracies = cross_val_score(gradient_booster, X_train, y_train, cv=5)
gradient_booster.fit(X_train,y_train)

print("Train Score:",np.mean(accuracies))
print("Test Score:",gradient_booster.score(X_test,y_test))

Train Score: 0.8647881398244366
Test Score: 0.8708736373407032

result_dict_train = {}
result_dict_test = {}
result_dict_train["Gradient-Boost Default Train Score"] = np.mean(accuracies)
result_dict_test["Gradient-Boost Default Test Score"] = gradient_booster.score(X_test,y_test)

grid = {
    'learning_rate':[0.01,0.05,0.1],
    'n_estimators':np.arange(100,500,100),
}

gb = GradientBoostingClassifier()
gb_cv = GridSearchCV(gb, grid, cv = 4)
gb_cv.fit(X_train,y_train)
print("Best Parameters:",gb_cv.best_params_)
print("Train Score:",gb_cv.best_score_)
print("Test Score:",gb_cv.score(X_test,y_test))

Best Parameters: {'learning_rate': 0.1, 'n_estimators': 400}
Train Score: 0.8711993243243243
Test Score: 0.8777828957469676

result_dict_train
result_dict_test

{'Gradient-Boost Default Test Score': 0.8708736373407032}